RDA Darling Downs and South West Region

Indicators - Estimate Resident Population

Populations are counted and estimated in various ways. The most comprehensive population count available in Australia is derived from the Population and Household Census conducted by the Australian Bureau of Statistics every 5 years.

However the Census count is not the official population of an area. To provide a more accurate population figure which is updated more frequently than every 5 years, the Australian Bureau of Statistics also produces "Estimated Resident Population" (ERP) numbers. Based on population estimates as at 30 June, ERPs take into account people who missed the count on Census night, including people who were temporarily overseas, plus an undercount adjustment for those who did not complete a Census form, and an overcount adjustment for anyone who was double counted.

Estimated Resident Population figures are updated annually taking into account births, deaths, internal and overseas migration. In addition, after every Census, ERP figures for the five previous years are "backcast", using information from the current Census, to ensure the most accurate figures are available.

Indicators - Gross Regional Product (GRP)

For the local area this information is synthesized by National Economics using a range of data sources (including ABS labour force survey, tax office and Centrelink datasets) to produce an estimate of the Gross Regional Product of the local economy. GRP is the equivalent of GDP at the local level for a Local Government Area or region, and the calculation method simulates that used for the nation, but is influenced by local characteristics such as types of employment and worker productivity. For more information see National Institute of Economic and Industry Research (NIEIR) (opens a new window)

To enable direct comparison between areas of varying size (eg. local, state, national), each year of data is divided by the base reference year and multiplied by 100 so that all areas are compared on the same scale. The actual size of the economy, and growth is shown in the box at the left. All data are expressed in constant dollar terms for the reference year (shown on the chart).

Changes to historical data

This dataset is underpinned by the NIEIR-ID economic model which is updated each financial year. In the 2016-17 update you can expect to see differences in some of the numbers to previous updates. For more details, see Economic model updates.

This dataset is underpinned by the NIEIR-ID economic model which is updated each financial year. In the 2016-17 update you can expect to see differences in some of the numbers to previous updates. For more details, see Economic model updates.

Indicators - Unemployment

Please note that the local unemployment data are sourced from Small Area Labour Markets, a quarterly publication by the Department of Employment and Workplace Relations. State and National figures are sourced from the ABS Labour Force Survey (Catalogue number 6202.0)(opens a new window). The Department of Employment data uses the labour force survey as a base and models it to local level using Centrelink data. Local unemployment is updated quarterly in this collection, and while state and national figures are available monthly, these are also updated on the site quarterly to match the time period of the local numbers. These benchmark figures are those widely published by government and media sites but are not directly comparable to the LGA estimates as they are not annual averages. For comparison purposes, LGA estimates should be compared with data in Table 16b of the publication ABS Labour Force, Detailed - Electronic Delivery publication (catalogue number 6291.0.55.001)(opens a new window) which presents 12-month averages of the benchmark regions.

All labour force data are subject to sampling error, as they are derived from a sample survey of approximately 29,000 dwellings nationwide.

All labour force data relate to the civilian population aged 15 years and over.

The definition of unemployment used is the standard ABS and international definition - Unemployed persons are defined as all persons aged 15 years and over who were not employed during the reference week, and either had actively looked for full-time or part-time work at any time in the four weeks up to the end of the reference week and were available for work in the reference week, or were waiting to start a new job within four weeks from the end of the reference week and could have started in the reference week if the job had been available then.

Employed persons are those aged 15 years or over who, during the survey reference week, worked for one hour or more for pay, profit or payment in kind in a job or business, or on a farm; or worked for one hour or more without pay in a family business or on a farm; or who had a job but were not at work for a number of specified reasons; or were employers or self-employed persons who had a job, business or farm, but were not at work.

The value of approval data includes all approved residential building valued at $10,000 or more and all approved non-residential building valued at $50,000 or more. Value of building work excludes the value of land, and also excludes landscaping, but includes site preparation costs. Both new dwellings and alterations and additions to existing dwellings are included in the residential approvals.

Data presented here are the "Original" series, which has not been seasonally adjusted or smoothed to remove anomalies. Seasonal adjustment is not available at a local level, so for comparison purposes the state and national figures shown here are also "Original". Seasonally adjusted and trend figures are more often reported on a national basis by the ABS and the media, so for this reason, the figures shown here may not match those often reported.

Indicators - Retail trade

(not available for local area)

Retail Trade trends are based on estimates of turnover compiled from the monthly Retail Business Survey (RBS) undertaken by the ABS. It estimates of the value of turnover of retail businesses classified by industry, and by state and territory. It is not available for local areas. See ABS Retail Trade catalogue number 8501.0 for more details.

Indicators - Consumer Price Index (CPI)

(not available for local area)

The Consumer Price Index (CPI) measures quarterly changes in the price of a 'basket' of goods and services which account for a high proportion of expenditure by the CPI population group (i.e. metropolitan households). This 'basket' covers a wide range of goods and services, arranged in the following eleven groups:

Food

Alcohol and tobacco

Clothing and footwear

Housing

Household contents and services

Health

Transportation

Communication

Recreation

Education

Financial and insurance services.

The Consumer Price Index is measured for Greater Capital City regions only, so the state capital is used as a proxy for the whole state, and the Australia-wide index is correctly termed as the "Weighted average of eight capital cities".

Gross Regional Product (GRP)

Headline Gross Regional Product is the sum of all industries' estimated value added, plus a factor for ownership of dwellings. The value of accommodation is a part of the economy, but it is not part of any industry, so it is included separately. Ownership of dwellings includes actual rents received by landlords, and imputed rents representing the ongoing value of owner-occupied housing.

Local GRP gives the value of economic activity which accrues to the local area after taxes and dividends are paid outside the area. It is normally lower than Headline GRP as it does not reflect those elements of economic productivity which accrue to public company shareholders and the federal government outside the area.

Comparing headline GRP to local GRP is a good way to see whether the economy in the area mainly comprises local businesses or large, multinational companies, and whether this is changing.

Residents GRP is the economic value-added generated by the residents of the area, regardless of where they work. Residents GRP is best thought of as the income received by people in the LGA. Areas with a high Residents GRP and low Local GRP probably export most of their residents (or at least their higher value workers) to jobs elsewhere, while areas with a high Local GRP and low Residents GRP mainly import their workers, or higher value work.

Local GRP per worker is derived by dividing the Local GRP by the estimate of the number of employed persons in the LGA in the relevant time period. This in turn is derived from the ABS Labour Force survey and State Accounts data.

This dataset forms part of the National Economics microsimulation model of the local economy, updated annually, and derived from ABS, ATO, Centrelink and other economic datasets.

Changes to historical data

This dataset is underpinned by the NIEIR-ID economic model which is updated each financial year. In the 2016-17 update you can expect to see differences in some of the numbers to previous updates. For more details, see Economic model updates.

Employment by industry (Total)

Employment data presented here are estimates based on National Economics modelling from a number of sources. They are NOT Census figures, and should not be directly compared to the employment numbers in the "Worker Profiles" section of economy.id. They are a more accurate and up-to-date estimate of employment in the LGA than the Census figures, and give a clear idea of the employment breakdown by industry, however it is not possible to derive the range of worker characteristics from them that are available in Census data, so both modelled and Census data on employment are presented as part of economy.id.

The estimates from NIEIR will generally be higher than the Census figures because they adjust for:

Persons missed by the Census

Persons who didn't state their employment status or place of work

Persons who reported no fixed place of work

Persons whose place of work was not a valid address which could be coded by the ABS.

Derivation:In general, the Census understates employment by 15-20%, including about 12.5% of known working population in the Census who could not be coded to a valid workplace.

Estimates of employment by industry from ATO income tax returns are used by postcode of residence and are converted to LGA of residence. Tax data is used because it is more timely and accurate than Census income data.

The Census undercount listed above is identified and allocated to workplace locations in accordance with the distribution for similar industry types of residents of the LGA who nominated a workplace. The results are then adjusted on a quarterly basis by employment results in the ABS Labour Force Survey. Estimates are also checked against commercial and industrial floorspace completion rates by location and industry in intercensal years.

Please note that these modelled estimates are subject to change. Estimates are reviewed when more recent and robust data becomes available, particularly when new National or State Accounts data are released by the ABS, or new tax office income data are released. Most recent financial year estimates are based on a combination of factors including Centrelink and Labour Force Survey data, which is replaced by ATO income data when it becomes available. As a result of this, revisions to the model for all years may be expected, and large revisions for the last two financial years are possible, when the new data are released each November, for the previous financial year. These revisions could change the statistical outcomes, so please treat the most recent years’ data with caution.

Location Quotient:The location quotient is a simple way of seeing which are the main industries in an area, relative to the wider region. It is similar to benchmarking, but in this case the percentage of the local economy (total employment, FTE or value-added) in a particular industry is divided by the percentage of the wider area (region, state, nation) that this industry makes up. A LQ of 1 indicates that the industry is about as prevalent in the local area as in the wider area. A LQ of greater than 1.2 indicates a significant specialisation of the industry, and an LQ of greater than 2 indicates a major specialisation. An LQ well under 1 indicates an industry which is more important in the region than the local area.

Location quotient is an optional way to display some datasets on economy.id. It can be chosen from the control panel.

Changes to historical data

This dataset is underpinned by the NIEIR-ID economic model which is updated each financial year. In the 2016-17 update you can expect to see differences in some of the numbers to previous updates. For more details, see Economic model updates.

Employment by industry (FTE)

Full-Time Equivalent employment is actually a representation of Hours Worked estimates from the National Economics modelling. It is considered an easier way to look at the aggregate hours worked data, which generally involves much larger numbers.

FTE employment is simply aggregate hours worked divided by 38 hours per week, as an estimate of the average full-time worker hours. Note that this means that workers doing more hours than this count in the data as more than one FTE job.

The starting point for estimating hours worked is the estimation of hours and dollars per hour reported at the 1-digit ANZSIC level for States and Territories from the ABS Labour Force Bulletin, which is updated quarterly. These are reconciled to the wages and salaries and mixed income series in the ABS Annual State Accounts, converted to $/hour by estimates of total hours worked by industry from the Labour Force Bulletin. The dataset is a smoothed 7 quarter moving average.

Hours of work by industry and dollars per hour by place of work were estimated using the Census journey to work matrix, adjusted for the net undercount, as described in more detail in the Employment data notes.

The NIEIR modelling uses the ABS Labour Force Survey and ATO data to estimate the number of hours worked in each industry in a given quarter. This is divided by 38 hours per week (representing a full-time week) to get the Full-Time Equivalent employment

As some industries have very high hours of work per individual, in these cases FTE employment can actually be higher than total employment. This is often the case in the Agriculture, Forestry and Fishing industry.

Please note that these modelled estimates are subject to change. Estimates are reviewed when more recent and robust data becomes available, particularly when new National or State Accounts data are released by the ABS, or new tax office income data are released. Most recent financial year estimates are based on a combination of factors including Centrelink and Labour Force Survey data, which is replaced by ATO income data when it becomes available. As a result of this, revisions to the model for all years may be expected, and large revisions for the last two financial years are possible, when the new data are released each November, for the previous financial year. These revisions could change the statistical outcomes, so please treat the most recent years’ data with caution.

Location Quotient:The location quotient is a simple way of seeing which are the main industries in an area, relative to the wider region. It is similar to benchmarking, but in this case the percentage of the local economy (total employment, FTE or value-added) in a particular industry is divided by the percentage of the wider area (region, state, nation) that this industry makes up. A LQ of 1 indicates that the industry is about as prevalent in the local area as in the wider area. A LQ of greater than 1.2 indicates a significant specialisation of the industry, and an LQ of greater than 2 indicates a major specialisation. An LQ well under 1 indicates an industry which is more important in the region than the local area.

Location quotient is an optional way to display some datasets on economy.id. It can be chosen from the control panel.

Changes to historical data

This dataset is underpinned by the NIEIR-ID economic model which is updated each financial year. In the 2016-17 update you can expect to see differences in some of the numbers to previous updates. For more details, see Economic model updates.

Employment by industry (Census)

Employment data is sourced from the ABS Census, 2016 and 2011. It is the total number of persons employed in an industry sector, within the local area. It is based on the ABS coding of workplace addresses, not on residential location. This dataset should NOT be compared with National Economics modelled datasets on the number of workers, which are a more accurate representation and updated every year. Census counts of employment are useful because they can break down to a more detailed level of industry classification than the modelled dataset, and can also present various demographic characteristics of the workers. However it needs to be remembered that they are an undercount of the true employment and exclude:

People who didn't state their employment status

People who were entirely missed in the Census

People who had no fixed place of work

Based on these differences, in most cases, the Census place of work figures are around a 10-15% undercount compared to NIEIR modelling. Modelling also includes Construction workforce on a different basis, where workers are allocated to the location of the construction activity, rather than the address of the company. Note that in the 2016 Census, people who did not state their place of work were “Imputed” to a place of work. So the total undercount should be less than in previous Censuses, but it is still subject to overall Census undercount, and people who stated no fixed place of work.

This table presents information at the ANZSIC 1-digit (division) level, with sub-categories available at the 3-digit (group) level. A total of 293 industry categories are available at this level, by clicking on the table entries, or exporting the full version of the table. Only division level data appear in the charts.

Value added by industry

Value added is the value of sales generated by each industry, minus the cost of its inputs. Estimates are modelled using the NIEIR methodology, and presented in constant dollars (adjusted for inflation). It is calculated by subtracting the cost of industry inputs from total sales generated.

The total of all industry value added is summed to produce Gross Regional Product figures, which also include an estimate for the ongoing value of ownership of dwellings in the area.

Value added data are derived from ABS State Accounts, distributed among regions by industry according to estimates of industry value added, Census and ABS Labour Force based employment and industry earnings data from the Australian Taxation Office. An estimate of labour productivity is derived from ATO data from each region and applied to local workers for the industry .

Please note that these modelled estimates are subject to change. Estimates are reviewed when more recent and robust data becomes available, particularly when new National or State Accounts data are released by the ABS, or new tax office income data are released. Most recent financial year estimates are based on a combination of factors including Centrelink and Labour Force Survey data, which is replaced by ATO income data when it becomes available. As a result of this, revisions to the model for all years may be expected, and large revisions for the last two financial years are possible, when the new data are released each November, for the previous financial year. These revisions could change the statistical outcomes, so please treat the most recent years’ data with caution.

Location Quotient:The location quotient is a simple way of seeing which are the main industries in an area, relative to the wider region. It is similar to benchmarking, but in this case the percentage of the local economy (total employment, FTE or value added) in a particular industry is divided by the percentage of the wider area (region, state, nation) that this industry makes up. A LQ of 1 indicates that the industry is about as prevalent in the local area as in the wider area. A LQ of greater than 1.2 indicates a significant specialisation of the industry, and an LQ of greater than 2 indicates a major specialisation. An LQ well under 1 indicates an industry which is more important in the region than the local area.

Location quotient is an optional way to display some datasets on economy.id. It can be chosen from the control panel.

Changes to historical data

This dataset is underpinned by the NIEIR-ID economic model which is updated each financial year. In the 2016-17 update you can expect to see differences in some of the numbers to previous updates. For more details, see Economic model updates.

Output

Output is the gross sales of an industry, which includes the cost of inputs to that industry. To the extent that outputs from one industry are used as inputs to another, the economic productivity of an industry may be counted multiple times in output, which is why output totals generally appear much higher than value add or GRP.

Output data are derived from ABS State Accounts, distributed among regions by industry according to estimates of industry value-added, Census and ABS Labour Force based employment and industry earnings data from the Australian Taxation Office. An estimate of labour productivity is derived from ATO data from each region and applied to to local workers for the industry.

Please note that these modelled estimates are subject to change. Estimates are reviewed when more recent and robust data becomes available, particularly when new National or State Accounts data are released by the ABS, or new tax office income data are released. Most recent financial year estimates are based on a combination of factors including Centrelink and Labour Force Survey data, which is replaced by ATO income data when it becomes available. As a result of this, revisions to the model for all years may be expected, and large revisions for the last two financial years are possible, when the new data are released each November, for the previous financial year. These revisions could change the statistical outcomes, so please treat the most recent years’ data with caution.

Location Quotient:The location quotient is a simple way of seeing which are the main industries in an area, relative to the wider region. It is similar to benchmarking, but in this case the percentage of the local economy (total employment, FTE or value-added) in a particular industry is divided by the percentage of the wider area (region, state, nation) that this industry makes up. A LQ of 1 indicates that the industry is about as prevalent in the local area as in the wider area. A LQ of greater than 1.2 indicates a significant specialisation of the industry, and an LQ of greater than 2 indicates a major specialisation. An LQ well under 1 indicates an industry which is more important in the region than the local area.

Location quotient is an optional way to display some datasets on economy.id. It can be chosen from the control panel.

Changes to historical data

This dataset is underpinned by the NIEIR-ID economic model which is updated each financial year. In the 2016-17 update you can expect to see differences in some of the numbers to previous updates. For more details, see Economic model updates.

Local sales

Local sales includes the gross economic output (sales) which are purchased by local consumers, businesses and government. Local sales is equal to total output minus total exports.

Please note that these modelled estimates are subject to change. Estimates are reviewed when more recent and robust data becomes available, particularly when new National or State Accounts data are released by the ABS, or new tax office income data are released. Most recent financial year estimates are based on a combination of factors including Centrelink and Labour Force Survey data, which is replaced by ATO income data when it becomes available. As a result of this, revisions to the model for all years may be expected, and large revisions for the last two financial years are possible, when the new data are released each November, for the previous financial year. These revisions could change the statistical outcomes, so please treat the most recent years’ data with caution.

Changes to historical data

This dataset is underpinned by the NIEIR-ID economic model which is updated each financial year. In the 2016-17 update you can expect to see differences in some of the numbers to previous updates. For more details, see Economic model updates.

Domestic exports

Exports are sales of goods and services to non-resident households, businesses and other organisations, outside the LGA boundaries. These sales include both local value added and the value of inputs, i.e. they are equivalent in magnitude to total sales, not just value added. Exports (domestic) includes all exports from the LGA or region to other parts of Australia.

Please note that these modelled estimates are subject to change. Estimates are reviewed when more recent and robust data becomes available, particularly when new National or State Accounts data are released by the ABS, or new tax office income data are released. Most recent financial year estimates are based on a combination of factors including Centrelink and Labour Force Survey data, which is replaced by ATO income data when it becomes available. As a result of this, revisions to the model for all years may be expected, and large revisions for the last two financial years are possible, when the new data are released each November, for the previous financial year. These revisions could change the statistical outcomes, so please treat the most recent years’ data with caution.

Changes to historical data

This dataset is underpinned by the NIEIR-ID economic model which is updated each financial year. In the 2016-17 update you can expect to see differences in some of the numbers to previous updates. For more details, see Economic model updates.

International exports

Exports are sales of goods and services to non-resident households, businesses and other organisations, outside the LGA boundaries. These sales include both local value added and the value of inputs, i.e. they are equivalent in magnitude to total sales, not just value added. Exports (international) includes all exports from the LGA or region to countries outside Australia.

Please note that these modelled estimates are subject to change. Estimates are reviewed when more recent and robust data becomes available, particularly when new National or State Accounts data are released by the ABS, or new tax office income data are released. Most recent financial year estimates are based on a combination of factors including Centrelink and Labour Force Survey data, which is replaced by ATO income data when it becomes available. As a result of this, revisions to the model for all years may be expected, and large revisions for the last two financial years are possible, when the new data are released each November, for the previous financial year. These revisions could change the statistical outcomes, so please treat the most recent years’ data with caution.

Changes to historical data

This dataset is underpinned by the NIEIR-ID economic model which is updated each financial year. In the 2016-17 update you can expect to see differences in some of the numbers to previous updates. For more details, see Economic model updates.

Domestic imports

Imports are purchases of goods and services from industries located outside the LGA boundaries, for use in the production function of that industry. Domestic imports are those which originate from other areas within Australia.

Imports of goods for direct on-sale is not included in the imports figure if they are not used directly in the goods and service production of that industry. Eg. Retail Trade is a service industry whose production is based on selling goods to consumers. The value of the goods themselves is not included in the imports total, only the value of goods and services used in providing this service – e.g. an accounting service, inventory management software, shop furnishings etc.

Please note that these modelled estimates are subject to change. Estimates are reviewed when more recent and robust data becomes available, particularly when new National or State Accounts data are released by the ABS, or new tax office income data are released. Most recent financial year estimates are based on a combination of factors including Centrelink and Labour Force Survey data, which is replaced by ATO income data when it becomes available. As a result of this, revisions to the model for all years may be expected, and large revisions for the last two financial years are possible, when the new data are released each November, for the previous financial year. These revisions could change the statistical outcomes, so please treat the most recent years’ data with caution.

Changes to historical data

This dataset is underpinned by the NIEIR-ID economic model which is updated each financial year. In the 2016-17 update you can expect to see differences in some of the numbers to previous updates. For more details, see Economic model updates.

Imports

Imports are purchases of goods and services from industries located outside the LGA boundaries, for use in the production function of that industry. International imports are those originating outside Australia.

Imports of goods for direct on-sale is not included in the imports figure if they are not used directly in the goods and service production of that industry. Eg. Retail Trade is a service industry whose production is based on selling goods to consumers. The value of the goods themselves is not included in the imports total, only the value of goods and services used in providing this service – e.g. an accounting service, inventory management software, shop furnishings etc.

Please note that these modelled estimates are subject to change. Estimates are reviewed when more recent and robust data becomes available, particularly when new National or State Accounts data are released by the ABS, or new tax office income data are released. Most recent financial year estimates are based on a combination of factors including Centrelink and Labour Force Survey data, which is replaced by ATO income data when it becomes available. As a result of this, revisions to the model for all years may be expected, and large revisions for the last two financial years are possible, when the new data are released each November, for the previous financial year. These revisions could change the statistical outcomes, so please treat the most recent years’ data with caution.

Changes to historical data

This dataset is underpinned by the NIEIR-ID economic model which is updated each financial year. In the 2016-17 update you can expect to see differences in some of the numbers to previous updates. For more details, see Economic model updates.

International imports

Imports are purchases of goods and services from industries located outside the LGA boundaries, for use in the production function of that industry. International imports are those originating outside Australia.

Imports of goods for direct on-sale is not included in the imports figure if they are not used directly in the goods and service production of that industry. Eg. Retail Trade is a service industry whose production is based on selling goods to consumers. The value of the goods themselves is not included in the imports total, only the value of goods and services used in providing this service – e.g. an accounting service, inventory management software, shop furnishings etc.

Please note that these modelled estimates are subject to change. Estimates are reviewed when more recent and robust data becomes available, particularly when new National or State Accounts data are released by the ABS, or new tax office income data are released. Most recent financial year estimates are based on a combination of factors including Centrelink and Labour Force Survey data, which is replaced by ATO income data when it becomes available. As a result of this, revisions to the model for all years may be expected, and large revisions for the last two financial years are possible, when the new data are released each November, for the previous financial year. These revisions could change the statistical outcomes, so please treat the most recent years’ data with caution.

Changes to historical data

This dataset is underpinned by the NIEIR-ID economic model which is updated each financial year. In the 2016-17 update you can expect to see differences in some of the numbers to previous updates. For more details, see Economic model updates.

Worker productivity

Worker productivity is calculated by dividing the industry value added by the average (mean) number of persons employed over the four quarters of the financial year. Regional differences in the worker productivity are inherent in the model, which is based on income tax return information from the ATO, relativities between industries calculated from Census data, and labour force survey information updated annually.

High worker productivity figures mean that fewer workers in that industry may produce a greater output. Mining and financial services industries tend to have high worker productivity figures. Please note that worker productivity figures will be generated if you have at least one worker in that industry in your area. Some areas may have very low numbers of workers in particular sectors, and therefore have highly variable worker productivity in those sectors. While the figures can give a guide as to which sectors could add the most value to the economy if grown, care should be taken in interpreting the figures for industries with very low current numbers of workers.

Please note that these modelled estimates are subject to change. Estimates are reviewed when more recent and robust data becomes available, particularly when new National or State Accounts data are released by the ABS, or new tax office income data are released. Most recent financial year estimates are based on a combination of factors including Centrelink and Labour Force Survey data, which is replaced by ATO income data when it becomes available. As a result of this, revisions to the model for all years may be expected, and large revisions for the last two financial years are possible, when the new data are released each November, for the previous financial year. These revisions could change the statistical outcomes, so please treat the most recent years’ data with caution.

Changes to historical data

This dataset is underpinned by the NIEIR-ID economic model which is updated each financial year. In the 2016-17 update you can expect to see differences in some of the numbers to previous updates. For more details, see Economic model updates.

Worker productivity per hour

Worker productivity per hour is calculated by dividing the total value added in an industry by the total number of hours worked over the four quarters of the financial year. Regional differences in the worker productivity are inherent in the model, which is based on income tax return information from the ATO, relativities between industries calculated from Census data, and labour force survey information updated annually.

High worker productivity figures mean that fewer workers in that industry may produce a greater output. Mining and financial services industries tend to have high worker productivity figures. Please note that worker productivity figures will be generated if you have at least one worker in that industry in your area. Some areas may have very low numbers of workers in particular sectors, and therefore have highly variable worker productivity in those sectors. While the figures can give a guide as to which sectors could add the most value to the economy if grown, care should be taken in interpreting the figures for industries with very low current numbers of workers.

Please note that these modelled estimates are subject to change. Estimates are reviewed when more recent and robust data becomes available, particularly when new National or State Accounts data are released by the ABS, or new tax office income data are released. Most recent financial year estimates are based on a combination of factors including Centrelink and Labour Force Survey data, which is replaced by ATO income data when it becomes available. As a result of this, revisions to the model for all years may be expected, and large revisions for the last two financial years are possible, when the new data are released each November, for the previous financial year. These revisions could change the statistical outcomes, so please treat the most recent years’ data with caution.

Changes to historical data

This dataset is underpinned by the NIEIR-ID economic model which is updated each financial year. In the 2016-17 update you can expect to see differences in some of the numbers to previous updates. For more details, see Economic model updates.

Business by industry

The ABS Business Register is extracted from the Australian Business Register maintained by the ATO. It is a count of businesses with an Australian Business Number (ABN) on the Australian Business Register (i.e. actively trading).

The ABS Business Register does not include:

entities without an ABN - mainly individuals whose business activities fall under the threshold for GST compliance and whose taxation obligations can be satisfied under the Personal Income Tax System

Entities not considered to be actively trading in the market sector, including:

Central Bank

General Government – this particularly affects data for Education and Health and Community Services and means that institutions such as public universities, public schools, public hospitals and other public education and health organisations are not included in this data.

Non-Profit Institutions Serving Households

Charitable Institution

Social and Sporting Clubs

Trade Unions and Other Associations

Other Unincorporated Entity

Diplomatic or Trade Missions, Other Foreign Government

Private Households Employing Staff

The ABS Business Register does include:

employing and non-employing businesses

Single location and multiple location businesses

Entities with complex business structure - the business is assessed and broken up into Type of Activity Units (TAUs). The statistical unit referred to as a "business" thus consists of ABNs and TAUs

IMPORTANT NOTE ABOUT GEOGRAPHIC AREAS:Business Register counts are published by the ABS on SA2 (Statistical Area level 2) boundaries, not Local Government Area boundaries. The data presented in economy.id aggregates SA2 level data to Local Government Areas. However, in some cases, SA2s do not align to LGA boundaries. Where an SA2 crosses an LGA boundary, an estimate has been made to apportion the businesses in an SA2 across two or more LGAs. The estimate is done on the basis of the proportion of commercial, industrial and agricultural land use falling on either side of a boundary but please be aware that this is an approximation only, and doesn’t take account of the differing distributions of different industry sectors, nor is it an accurate count of individual businesses on either side of a boundary. For this reason, the business counts used in economy.id may not be an exact match to counts sourced directly from the Australian Taxation Office. Some areas are more affected by this issue than others. For more information on whether your area is affected, please contact .id.

Industry sector analysis

For detailed definitions of each of the parameters included in the table, please see the information box located to the left of the listed item.

This section presents the characteristics of a single industry (1 or 2 digit) within the area, based on the NIEIR microsimulation modelling.

Total employment, FTE employment, total output, value added, imports and exports are presented, both in raw number terms, and as a percentage of the same industry in the benchmark.

The idea behind this section is to show all the key characteristics of an industry sector in one place, to show a comparison of the local or export focus of an industry compared to the benchmark, and to show what percentage of the regional benchmark an industry makes up, in terms of different parameters.

Please note that Local Sales, Domestic Imports and Domestic Exports relate to the boundaries of the LGA – ie. Local Sales are those within the LGA, domestic exports are those going outside the LGA boundaries to other parts of Australia etc. For the benchmark area, these figures don’t relate to the boundaries of the state or region, but are best envisaged as the weighted average of the local sales, exports or imports for all LGAs in that state or region

Value-added is the value of sales generated by each industry, minus the cost of its inputs. Estimates are modelled using the NIEIR methodology, and presented in constant dollars (adjusted for inflation). It is calculated by subtracting the cost of industry inputs from total sales generated.

The total of all industry value-adds are summed to produce Gross Regional Product figures, which also include an estimate for the ongoing value of ownership of dwellings in the area.

The inputs to the microsimulation model used to derive this dataset include:

ABS National and state accounts

ABS Labour Force survey (employment)

ATO earnings data by industry

Building approvals by floorspace

Dun & Bradstreet business startup information.

Centrelink employment estimates (for the most recent year).

Please note that these modelled estimates are subject to change. Estimates are reviewed when more recent and robust data becomes available, particularly when new National or State Accounts data are released by the ABS, or new tax office income data are released. Most recent financial year estimates are based on a combination of factors including Centrelink and Labour Force Survey data, which is replaced by ATO income data when it becomes available. As a result of this, revisions to the model for all years may be expected, and large revisions for the last two financial years are possible, when the new data are released each November, for the previous financial year. These revisions could change the statistical outcomes, so please treat the most recent years’ data with caution.

Changes to historical data

This dataset is underpinned by the NIEIR-ID economic model which is updated each financial year. In the 2016-17 update you can expect to see differences in some of the numbers to previous updates. For more details, see Economic model updates.

Employment locations

Work Destination Zones (DZNs) are defined by the Road Traffic Authority in each state (eg. Vicroads, Main Roads WA etc). Work destinations in the Census are coded to these zones as the lowest level datasets available for workers by place of work.

The zones are defined with more detail in high employment areas, with the aim of being most useful for transport planning, particularly in areas where large numbers of people need to be moved from place of residence to place of work.

For this reason the size and density of zones vary enormously from place to place. For example, the CBD of Sydney might have a hundred or more separate zones, covering every street block. While a rural Local Government Area might all be covered by one or two destination zones. Generally in urban areas there are at least a few zones for each suburb, with more in major employment centres. In rural areas, there is usually at least a zone covering each small town, and one or more covering a large rural area.

The data presented here shows, by industry, the work destination of workers in by destination zone.

Please note that a new, experimental dataset has been used for this section of economy.id. This dataset eliminates all “Not stated”, “Zone undefined”, and “Place of work undefined” categories in the Census, and allocates all workers to a zone. This is done by imputing records to a zone even where workers did not state an address. The methodology involves looking at the work destinations of similar combinations of detailed industry, occupation and method of travel and making an “educated guess” (via an algorithm) at the likely work locations of populations where the exact address couldn’t be coded from the response.

This imputation was done by the ABS, in consultation with the Bureau of Transport Statistics, NSW (but the work was done for the whole country). For more information, please see the Journey to Work methodology on the BTS page.

As this methodology has not been rolled out on the rest of the economy.id site at present, there will be some small differences between the calculation of percentages of total on the spatial economy page, and that which would be derived by using a total from one of the other Census worker population pages. There will also be differences due to the allocation of DZNs to local government areas. In some cases these zones cross LGA boundaries, and a best fit which encompasses all the major employment areas in has been used here.

Value-added data is presented in millions of dollars ($m) and represents an approximation of the value of economic activity occurring within the boundaries of each destination zone. The estimate is based on

The imputed Census worker count within that zone.

The worker productivity estimate per worker from the NIEIR modelled dataset.

The distribution of industries within that zone.

Workers with no usual address don’t contribute directly to any one zone and so are excluded from this calculation, but they are included in overall worker numbers in the modelling and therefore in total value added. For this reason, and the fact that even imputed Census worker numbers don’t exactly match workers in the model, the sum of destination zone value-added will not equal the total value-added for the LGA, as found in the industry structure page.

This dataset also makes the assumption that worker productivity in a particular industry sector is the same for workers in different parts of the LGA. This is a generalisation of reality. The calculation also doesn’t take into account the geographic interactions between zones which generate value as well.

Please use the value-added estimates as a guide only.

Workers place of residence

This dataset describes the place of usual residence of employed persons in the selected industry or occupation. Journey to Work data is created by cross tabulating a person’s main workplace address (Place of Work Data) with their place of usual residence to create a matrix of home to work.

The dataset is presented at the Local Government Area (LGA) level. This information is generally not available at the small area (suburb/locality) level due to geographic limitations when being coded or processed.

Also please note that the number of workers in this section is subject to Census undercount, and is generally less than that found in the modelled dataset from National Economics.

Residents place of work

This data describes the work location (LGA/SLA) of employed residents of the local area in the selected industry or occupation. Journey to Work data is created by cross tabulating a person’s main workplace address (Place of Work Data) with their place of usual residence to create a matrix of home to work.

The dataset is presented at the Statistical Local Area (SLA) level. SLAs are either whole LGAs or parts of LGAs and presenting the data at this level can show movements within the LGA for larger councils, as well as movement outside the LGA. This information is generally not available at the small area (suburb/locality) level due to geographic limitations when being coded or processed.

Jobs to workers ratio

This dataset describes the residential location (LGA) of people who work in the local area. It differs from the main journey to work dataset in that it shows simply the number and proportion of workers in each broad occupation category who also live within the local area.

Journey to Work data is created by cross tabulating a person’s main workplace address (Place of Work Data) with their place of usual residence to create a matrix of home to work. This information is generally not available at the small area (suburb/locality) level due to geographic limitations when being coded or processed.

Self-sufficiency in economy.id is defined as the percentage of the local resident workers employed within the local LGA or region. The data presented here shows a time series, allowing the user to see whether the level of self-sufficiency in a particular industry has increased or decreased over time. The change over time is presented as a change in percentage rather than absolute number, so that self-sufficiency can be assessed independently from changes in the overall workforce in that industry.

Please note that the quality of Journey to Work coding has varied from Census to Census. The 2011 coding was particularly poor, with a large percentage of employed people being coded to "Place of Work undefined" categories. As people in these categories do feature in the theoretical working population of any area, this can affect the comparison of self-sufficiency over time.

ALTERNATE DEFINITION: Some state governments, for example, Western Australia, mandate a different (and equally valid) definition of self-sufficiency. This is the total number of jobs in the area divided by the total number of employed residents, regardless of where those residents work. This definition is equivalent in economy.id to our definition of “Employment Capacity”, so Western Australian users looking for “self-sufficiency” should see this topic instead.

This dataset is underpinned by the NIEIR-ID economic model which is updated each financial year. In the 2016-17 update you can expect to see differences in some of the numbers to previous updates. For more details, see Economic model updates.

Self-containment- Occupation

This dataset describes the work location (LGA) of employed residents of the local area. It differs from the main journey to work dataset in that it shows simply the number and proportion of residents in each broad occupation group who work within the local area and working outside it, rather than detailed destination information. Journey to Work data is created by cross-tabulating a person’s main workplace address (Place of Work Data) with their place of usual residence to create a matrix of home to work. This information is generally not available at the small area (suburb/locality) level due to geographic limitations when being coded or processed.

Self-containment is defined in economy.id as the percentage of local resident workers who work within the local LGA or region.

Please note that the quality of Journey to Work coding has varied from Census to Census. The 2011 coding was particularly poor, with a large percentage of employed people being coded to "Place of Work undefined" and “Not Stated” categories. The 2016 Census used a different methodology to impute workplace location where it was not provided by the respondent. For this reason, care should be taken when comparing 2016 to earlier years – when using raw 2011 data, there will be an apparent increase which may not reflect reality, but simply the coding methods. .id have sourced 2011 data which reduces this issue, but users should still be aware that these are different datasets with different methodologies.

Even with this updated methodology, although you will find no workers with “Not stated” place of work, there are still some who genuinely have “No fixed place of work” but travel around to work. These are NOT regarded as self-contained and so don’t form part of the self-containment percentage. Even a fully self-contained economy would therefore not show 100% self-containment due to this category, which often numbers around 4-5% of all employed residents.

Self-containment - Industry

This dataset describes the work location (LGA) of employed residents of the local area. It differs from the main journey to work dataset in that it shows simply the number and proportion of residents working in each industry who work within the local area and working outside it, rather than detailed destination information. Journey to Work data is created by cross-tabulating a person’s main workplace address (Place of Work Data) with their place of usual residence to create a matrix of home to work. This information is generally not available at the small area (suburb/locality) level due to geographic limitations when being coded or processed.

Self-containment is defined in economy.id as the percentage of local resident workers who work within the local LGA or region.

Please note that the quality of Journey to Work coding has varied from Census to Census. The 2011 coding was particularly poor, with a large percentage of employed people being coded to "Place of Work undefined" and “Not Stated” categories. The 2016 Census used a different methodology to impute workplace location where it was not provided by the respondent. For this reason, care should be taken when comparing 2016 to earlier years – when using raw 2011 data, there will be an apparent increase which may not reflect reality, but simply the coding methods. .id have sourced 2011 data which reduces this issue, but users should still be aware that these are different datasets with different methodologies.

Even with this updated methodology, although you will find no workers with “Not stated” place of work, there are still some who genuinely have “No fixed place of work” but travel around to work. These are NOT regarded as self-contained and so don’t form part of the self-containment percentage. Even a fully self-contained economy would therefore not show 100% self-containment due to this category, which often numbers around 4-5% of all employed residents.

Self-sufficiency - Occupation

This dataset describes the residential location (LGA) of people who work in the local area. It differs from the main journey to work dataset in that it shows simply the number and proportion of workers in each broad occupation category who also live within the local area.

Journey to Work data is created by cross-tabulating a person’s main workplace address (Place of Work Data) with their place of usual residence to create a matrix of home to work. This information is generally not available at the small area (suburb/locality) level due to geographic limitations when being coded or processed.

Self-sufficiency in economy.id is defined as the percentage of the local workers employed in the area who also live within the local LGA or region.

Please note that the quality of Journey to Work coding has varied from Census to Census. The 2011 coding was particularly poor, with a large percentage of employed people being coded to "Place of Work undefined" and “Not Stated” categories. The 2016 Census used a different methodology to impute workplace location where it was not provided by the respondent. For this reason, care should be taken when comparing 2016 to earlier years – when using raw 2011 data, there will be an apparent increase which may not reflect reality, but simply the coding methods. .id have sourced 2011 data which reduces this issue, but users should still be aware that these are different datasets with different methodologies.

ALTERNATE DEFINITION: Some state governments, for example, Western Australia, mandate a different (and equally valid) definition of self-sufficiency. This is the total number of jobs in the area divided by the total number of employed residents, regardless of where those residents work. This definition is equivalent in economy.id to our definition of “Jobs to Workers ratio”, so Western Australian users looking for “self-sufficiency” should see this topic instead.

Self-sufficiency - Industry

This dataset describes the residential location (LGA) of people who work in the local area. It differs from the main journey to work dataset in that it shows simply the number and proportion of workers in each broad industry category who also live within the local area.

Journey to Work data is created by cross-tabulating a person’s main workplace address (Place of Work Data) with their place of usual residence to create a matrix of home to work. This information is generally not available at the small area (suburb/locality) level due to geographic limitations when being coded or processed.

Self-sufficiency in economy.id is defined as the percentage of the local workers employed in the area who also live within the local LGA or region.

Please note that the quality of Journey to Work coding has varied from Census to Census. The 2011 coding was particularly poor, with a large percentage of employed people being coded to "Place of Work undefined" and “Not Stated” categories. The 2016 Census used a different methodology to impute workplace location where it was not provided by the respondent. For this reason, care should be taken when comparing 2016 to earlier years – when using raw 2011 data, there will be an apparent increase which may not reflect reality, but simply the coding methods. .id have sourced 2011 data which reduces this issue, but users should still be aware that these are different datasets with different methodologies.

ALTERNATE DEFINITION: Some state governments, for example, Western Australia, mandate a different (and equally valid) definition of self-sufficiency. This is the total number of jobs in the area divided by the total number of employed residents, regardless of where those residents work. This definition is equivalent in economy.id to our definition of “Jobs to Workers ratio”, so Western Australian users looking for “self-sufficiency” should see this topic instead.

Local workers- Key statistics

This data summarises the demographic characteristics of people employed in the selected industry division (or all industries). Includes all persons working in the area regardless of where they live. Some of the figures in the summary table are taken from other topics. For those which don’t appear elsewhere, the following notes apply:

Persons – people aged 15 and over who were employed in the week prior to Census

Individual income – Median income is the midpoint of incomes for all employed people.

Local workers - Age

This dataset describes the age (by sex) of people employed in the selected industry (or all industries if selected). It includes all persons working in the area regardless of where they live.

Employment is applicable to all persons over the age of 15. Generally, relatively few workers are over the traditional retirement age of 65, but some industries (eg. Agriculture) have a high proportion in this cohort.

Local workers - Age

This dataset describes the age (by sex) of people employed in the selected industry (or all industries if selected). It includes all persons working in the area regardless of where they live.

Employment is applicable to all persons over the age of 15. Generally, relatively few workers are over the traditional retirement age of 65, but some industries (eg. Agriculture) have a high proportion in this cohort.

Local workers - Hours Worked

This data describes the working hours (by sex) of employed persons employed in the selected industry. It includes all persons working in the local area regardless of where they live, and relates specifically to hours worked the week prior to the Census. It is therefore specific to a time period and does not necessarily reflect the number of hours worked in an average week. If employed persons were away from work during Census week, hours worked will be lower. This dataset relates to all jobs the worker holds, not just the main job referred to in the industry classification.

Workers are classified as full-time if they worked 35 hours or more in the week prior to Census, and part time if they worked less than this.

Note that the hours worked data relates to "all jobs", while the industry counted is what the respondent stated as their "main job".

Local workers - Occupations

This data describes the occupations (by sex) of people employed in the selected industry. It includes all persons working in the local area regardless of where they live. Relates to the main job held in the week prior to Census. Data for occupations are coded using the Australian and New Zealand Standard Classification of Occupations (ANZSCO). The occupation classification is updated periodically to take account of emerging occupation groups and changes to the structure of the labour force.

Data are presented for the broad occupation groupings, which are broadly based on the education or skill level required to do a particular job

Local workers - Qualifications

This data describes the level of the highest qualification (by sex) of employed persons in the selected industry. It includes all persons working in the local area regardless of where they live.

Qualifications are broken down by skill level, according to the Australian Standard Classification of Education (ASCED)(opens a new window), (catalogue number 1272.0). Bachelor degree and higher level qualifications are generally provided by universities, while diploma level qualifications can be gained through universities or TAFE colleges. Certificate level qualifications are vocational based qualifications usually gained through TAFE and apprenticeships. Examples of particular occupations requiring certificate level qualifications are shown below:

Local workers - Field of qualifications

This dataset describes the field of study of the highest qualification completed of employed persons in the selected industry. The dataset includes all persons working in the local area regardless of where they live.

At the broad (1-digit) level, presented on the site, categories in field of study are distinguished from each other on the basis of the theoretical content of the course and the broad purpose for which the study is undertaken.

At the narrow (4-digit) level, presented on the site through drill-down, fields of study are distinguished from other narrow fields within the same broad field of study on the basis of the objects of interest and the purpose for which the study is undertaken.

Note that the field of qualification relates only to the highest qualification the person has received. For example, a person with a bachelor degree in engineering and a graduate diploma in education, would have only the education qualification recorded in the Census.

Local workers - Weekly income

This data describes the total gross weekly income by sex (including pensions and allowances) of employed persons employed in the selected industry. It includes all persons working in the local area regardless of where they live. It should not be assumed that wages and salaries are a person’s only source of income.

Local workers - Weekly income quartiles

Incomes of workers are not comparable over time because of the influences of economic change such as wage level fluctuations and inflation. In addition, the ABS uses different ranges to collect income data every Census. The income quartile method has been adopted as the most objective method of comparing change in the income profile of local workers over time, relative to a benchmark area.

Individual income quartiles look at the distribution of incomes for workers in the selected industry within RDA Darling Downs and South West Region relative to the state average. Quartiles split the total number of workers into four equal parts for the total local workers(all industries) in Queensland. The table shows the number and proportion of workers in the selected industry in the RDA Darling Downs and South West Region falling into each segment, with a comparison to a benchmark industry, or the same industry in a different region.

Benchmarks are recalculated for each industry, so that 25% of that industry's workers fall into each quartile across the state. For example, if the retail trade industry has 30% of workers in the lowest category and only 10% in the highest category, this indicates that the incomes in retail in RDA Darling Downs and South West Region are generally lower than the retail local workers across Queensland. The total of workers in all industries is only used to calculate the four quartiles. Once the dollar values have been established for these quartiles, it is possible to make meaningful comparisons to any industry or area, based on the inflation adjusted parameters.

Local workers - Method of travel to work

This dataset describes the method of travel to work of people employed in the selected industry. It includes all persons working in the local area regardless of where they live.

Method of travel relates specifically to the journey to work on the morning of Census day. Respondents can nominate up to three modes of travel, resulting in 234 separate categories in the full classification, being all combinations of 1,2 and 3 methods. Most people take only one or two methods of travel, and the table presented aggregates the most popular multiple methods when combined with public transport use. Other combinations of 2 and 3 methods, not involving train or bus, are aggregated in the “Other”.

While this has most of the categories in the full classification, it only includes 0.7% of workers in Australia. The methods of travel “Walked only” and “Worked at home” are exclusive and not combined with any other methods. “Did not go to work” relates to the day of Census only.

Resident workers - Key statistics

This dataset summarises the demographic characteristics of people in the local labour force. It includes people in the labour force who usually reside in the local area regardless of where they work (if working).

Some of the figures in the summary table are taken from other topics in the worker and labour force profiles sections of economy.id - please refer to the relevant data notes for those topics. For those which don’t appear elsewhere, the following notes apply:

Persons – persons in the labour force (persons aged 15 years and over who are looking for work, or are employed, full time, part-time or casually) who reside in the local area.

Individual income – low and high quartiles relate to those people earning in the lowest and highest 25% of incomes in the state respectively

Resident workers - Industry

This dataset describes the industries (by sex) in which employed residents work. It applies only to people aged 15 and over who were employed in the week prior to Census and includes employed people who usually reside in the local area regardless of where they work.

Data for industry are coded using the Australia and New Zealand Standard Industrial Classification (ANZSIC). The industry classification is updated periodically to take account of emerging industries and changes in the structure of the economy.

This table presents information at the ANZSIC 1-digit (division) level, with sub-categories available at the 3-digit (group) level. A total of 293 industry categories are available at this level, by clicking on the table entries, or exporting the full version of the table. Only division level data appear in the charts.

Resident workers - Hours worked

This dataset describes the working hours (by sex) of employed residents. It applies only to people aged 15 and over who were employed in the week prior to Census. It includes employed people who usually reside in the local area regardless of where they work, and relates specifically to hours worked the week prior to the Census. It is therefore only an indicator of full/part time status and does not necessarily reflect the number of hours worked in an average week. If employed persons were away from work during Census week, hours worked will be lower. Hours worked relates to all jobs the worker holds, not just the main job referred to in the industry classification.

Workers are classified as full-time if they worked 35 hours or more in the week prior to Census, and part time if they worked less than this.

Resident workers - Occupation

This data describes the occupations (by sex) in which employed residents work. It applies only to people aged 15 and over who were employed in the week prior to Census and includes employed people who usually reside in the local area regardless of where they work. Relates to the main job held in the week prior to Census.

Data for occupations are coded using the Australian and New Zealand Standard Classification of Occupations (ANZSCO). The occupation classification is updated periodically to take account of emerging occupation groups and changes to the structure of the labour force.

Data are presented for the broad occupation groupings. Occupations are ranked in descending order of the approximate level of skill or education required.

Resident workers - Qualification

This dataset describes the level of the highest qualification (by sex) of persons in the local labour force. It includes people in the labour force who usually reside in the local area regardless of where they work (if working).

Qualifications are broken down by skill level, according to the Australian Standard Classification of Education (ASCED)(opens a new window), (catalogue number 1272.0). Bachelor degree and higher level qualifications are generally provided by universities, while diploma level qualifications can be gained through universities or TAFE colleges. Certificate level qualifications are vocational based qualifications usually gained through TAFE and apprenticeships. Examples of particular occupations requiring certificate level qualifications are shown below:

At the broad (1-digit) level, presented on the site, categories in field of study are distinguished from each other on the basis of the theoretical content of the course and the broad purpose for which the study is undertaken.

At the narrow (4-digit) level, presented on the site through drill-down, fields of study are distinguished from other narrow fields within the same broad field of study on the basis of the objects of interest and the purpose for which the study is undertaken.

Note that the field of qualification relates only to the highest qualification the person has received. For example, a person with a bachelor degree in engineering and a graduate diploma in education, would have only the education qualification recorded in the Census.

Resident workers - Income

This data describes the total gross weekly income by sex (including pensions and allowances) of persons in the local labour force. It includes people in the labour force who usually reside in the local area regardless of where they work (if working). It should not be assumed that wages and salaries are a person’s only source of income.

Resident workers - Income

Incomes of employed people are not comparable over time because of the influences of economic change such as wage level fluctuations and inflation. In addition, the ABS uses different ranges to collect income data every Census. The income quartile method has been adopted as the most objective method of comparing change in the income profile of a labour force over time, relative to a benchmark area.

Individual income quartiles look at the distribution of incomes for employed residents of RDA Darling Downs and South West Region relative to the state average. Quartiles split the total number of employed residents into four equal parts for Regional QLD. The table shows the number and proportion of workers in the RDA Darling Downs and South West Region falling into each segment, with a comparison to the benchmark area. If the benchmark chosen is Regional QLD, by definition, there are 25% in each quartile for this area. If a different benchmark is chosen, benchmark percentages may vary.

For example, the income profile of residents in an area may show 40% in the lowest income quartile, which indicates that the area has a higher proportion of low income residents than the Regional QLD average. However if a comparison to the region showed 50% in the lowest income quartile, then the employed residents of the area are higher income than the benchmark.

Resident workers - Method of travel to work

This dataset describes the method of travel to work of people employed in the selected industry. It includes all persons working in the local area regardless of where they live.

Method of travel relates specifically to the journey to work on the morning of Census day. Respondents can nominate up to three modes of travel, resulting in 234 separate categories in the full classification, being all combinations of 1,2 and 3 methods. Most people take only one or two methods of travel, and the table presented aggregates the most popular multiple methods when combined with public transport use. Other combinations of 2 and 3 methods, not involving train or bus, are aggregated in the “Other”.

While this has most of the categories in the full classification, it only includes 0.7% of workers in Australia. The methods of travel “Walked only” and “Worked at home” are exclusive and not combined with any other methods. “Did not go to work” relates to the day of Census only.

Resident workers - Unemployed key statistics

This dataset summarises the demographic characteristics of unemployed people in the local labour force. It includes only unemployed people who usually reside in the local area.

Data notes for many of the topics are the same as those found elsewhere in the worker profiles and local labour force. The following specific notes relate to the unemployed key statistics only.

To be classified as unemployed, a person must have been not working in the week prior to Census, actively looking for work and available to start work in the next 4 weeks.

Unemployment relates to persons over the age of 15, but for “Highest level of schooling” it has been further restricted to persons over the age of 18, the age at which most people leave school. The category “Still attending” covers those who are still at school but also looking for work.

Speaks a language other than English at home – this is a measure of the population (unemployed population in this case) who have a first or primary language other than English. It does not imply anything about English proficiency, only that a different language is spoken by the person in their household.

Has broadband internet access at home – relates to the dwelling in which the person was counted. This may be a multiple count for some dwellings with multiple unemployed people in them.

Has child care responsibilities – this is a count of unemployed people who answered the question “In the last two weeks did the person spend time looking after a child, without pay?”, with “Yes, own children”, “Yes, others’ children” or “Yes, own and others’ children”.

Local market - Key statistics

Age structure

Describes the age structure (by sex) of people who usually reside in the local area. Includes all persons except 'overseas visitors'.

Education institute attending

Describes the education institutions attended (by sex) by people who usually reside in the local area. Excludes 'overseas visitors'.

'Catholic' refers to infant, primary and secondary schools run independently by the Catholic Church.

'Independent' refers to private and other non-Government schools.

'TAFE' refers to 'Technical and Further Education' institutions.

Proficiency in English

English proficiency aims to measure the ability of persons who speak ‘English as a Second Language’ to also speak English. The data, when viewed with other ethnic and cultural indicators, tends to reflect the ethnic composition of the population and the number of years of residence in Australia. In general, an area with a higher proportion of persons born in English-speaking countries or who emigrated from non-English speaking countries several decades ago is likely to have greater English-speaking proficiency.

Note: A person’s English proficiency is based on a subjective assessment and should therefore be treated with caution.

Responses to the question on Proficiency in English in the Census are subjective. For example, one respondent may consider that a response of 'Well' is appropriate if they can communicate well enough to do the shopping, while another respondent may consider such a response appropriate only for people who can hold a social conversation. Proficiency in English should be considered as an indicator of a person's ability to speak English and not a definitive measure of this ability.

Employment status (hours worked)

Describes the employment status (by sex) of people who usually reside in the local area. Excludes 'overseas vsitors'.

Includes persons aged 15 years and over.

'Employed full time' is defined as having worked 35 hours or more in all jobs during the week prior to Census night. 'Unemployed' includes those not employed and actively looking for work, while 'Not in the labour force' includes all those people not employed and not looking for work, including retirees, students, home duties, discouraged jobseekers etc.

Qualifications

Describes the qualifications (by sex) of people who usually reside in the local area. Includes persons aged 15 years and over.

Describes the household income (by sex) of people who usually reside in the local area.

Household income comprises the total of incomes of all persons in the household who stated an income. Excludes ‘visitor only households’ and ‘other non classifiable households’.

'Not stated' includes 'partial income not stated' and 'all incomes not stated'.

'Partial income not stated' includes households where at least one, but not all, member(s) aged 15 years and over did not state an income and / or at least one household member aged 15 years and over was temporarily absent. In these cases, the aggregate of all stated individual incomes would be less than the true household income so these households are excluded from the classification.

'All incomes not stated' includes households where no members present stated an income.

Housing tenure

Describes the housing tenure of occupied private dwellings in the local area. ‘Purchasing’ includes dwellings with a mortgage and those being purchased under a rent/buy scheme.

'Renting' includes both public and private rental, and people renting from an employer. Other categories including rent-free occupancy and life tenure schemes are not shown in this summary.

Dwelling structure

Describes the dwelling structure of all occupied private dwellings in the local area. This data is classified by the Census collector on visiting the household, and the categories are broadly based on the density of the housing types.

'Separate house' includes all free-standing dwellings separated from neighboring dwellings by a gap of at least half a metre.

'Medium density' includes all semi-detached, row, terrace or townhouses and flats in a one or two storey block.

'High density' includes all flats/apartments in a 3 or more storey block.

Sources of income

Household Disposable income data is based on the National Institute for Economic and Industry Research micro-simulation modelling.

The base data source is the ABS Household Expenditure Survey (HES), (6535.0) conducted every 5 years. These are adjusted based on quarterly estimates of the relative composition of household types within the LGA derived from the ABS Labour Force Monthly Survey (and expenditure profiles of those household types in the HES).

The derived figures are updated by total LGA household disposable income derived from the other models, and updated quarterly based on changes in state level household consumption figures in the ABS State Accounts data.

Household Disposable income is an assessment of the average income available to each household, based on economic activity undertaken by the residents.

It should not be directly compared to Census data, and is usually considerably higher because:

Census data is collected in ranges, and underestimates the incomes at the top end of the scale, which are factored in to data derived from the Labour Force Survey and ATO.

Household Disposable income includes an estimate for the imputed value of ownership of dwellings, which forms part of the wealth of households but isn’t factored into their Census income.

Household Disposable income also includes superannuation payments which are often not included in Census stated income.

Over 10% of households in Census don’t state an income, and these incomes are included in the modelled data derived from the ATO and ABS Labour Force Survey data.

Household disposable income can, however be directly compared between areas and over time, which is presented on this site.

Sources used in the model:

ABS Household Expenditure Survey

ABS Labour Force Survey

ABS State Accounts

Census of Population and Housing

Changes to historical data

This dataset is underpinned by the NIEIR-ID economic model which is updated each financial year. In the 2016-17 update you can expect to see differences in some of the numbers to previous updates. For more details, see Economic model updates.

Household expenditure

The Household Expenditure Survey is run by the ABS every 5 years, and measures, by income level, the expenditure of households on various expenditure items. The NIEIR economic model adjusts the expenditure based on the economic and household characteristics of the local area to provide an estimate of local expenditure.

Please note that the calculation of net savings presented here is a broad indicator only. Disposable income includes components for the value of owned dwellings and also includes Superannuation which is forced saving. The measure is useful for comparing between geographic areas, but should not be taken as an exact measure of the amount of money the average household saves outside the superannuation system.

Changes to historical data

This dataset is underpinned by the NIEIR-ID economic model which is updated each financial year. In the 2016-17 update you can expect to see differences in some of the numbers to previous updates. For more details, see Economic model updates.

Housing values and rentals

Hometrack's property database is processed from a range of different sources. All data is externally sourced. Data sources include government sources, advertised listings and rents, geospatial data sets, surveys and valuations.

Rents are defined as advertised rents, and value is defined as the value of properties according to Hometrack’s automated valuation model (AVM). The statistics shown on this site are calculated bottoms-up using Hometrack’s address-level baseline data. For example, the median value for a unit in a local government area is the value for which half the units have a valuation above and half below that value, the 75th percentile having a quarter above and three-quarters below, and so on.

This dataset is based on Hometrack's valuation database - which uses sales data and a proprietary algorithm to estimate housing values for all dwellings in an area.

Rental values represent weekly rates, derived from published rental listings, and yield is calculated based on the estimated value of each rental listing, and provided as a first quartile, median and third quartile here.

Housing sales and rental data are presented on an annual basis, at June of each year, based on automated valuation of all dwellings during that month, and rental listings for the June quarter (April/May/June of the reference year).

For some small local government areas, there may not be enough sales in a month, or enough rental listings to provide a reasonable valuation. In this case, data are not available.

These data relate to private rentals listed in April-June quarter in the reference year only. They exclude public and community housing, and exclude current rents being paid by tenants not advertised, and so the rental figures derived will differ from those sourced from Census.

Economic impact model

Economic impact modelling is based on Input-Output tables, a component of the NIEIR microsimulation model derived from local differences between industries and Census journey to work data in the local economy. An input-output matrix describes how the different industries in an economy interrelate, and how supply chains operate in the local area. The microsimulation economic modelling reproduces the National Accounts data for local areas. Data sources in the model include:

Census Journey to Work data

ABS Labour Force Survey

Centrelink employment estimates

ABS building approvals – commercial floorspace estimates.

Dun & Bradstreet Business Startups

Australian Taxation Office worker income data

Microsimulation of known large employers.

The modelling produces a factor, which shows the flow-on effects of economic productivity in an industry sector, to other sectors and the total economy. The impact of local production on areas outside the local area is also modelled, based on Journey to Work information from the Census, updated for known more recent employment projects.

Please note that these results are theoretical only, and is meant to give a broad indication of the type of flow-on effects which may apply in the economy if certain industries are expanded or reduced. Where an industry currently has a very small number of jobs or output in the local economy, the results from this model should be treated with caution, as very little data is available. Where there is currently no employment or output in a particular industry, a result cannot be calculated.

While the model will accept any input, no checking is done to see how reasonable this input assumption is. It is also really intended only to model relatively minor changes in jobs and size of industries in the short term (less than 10% of total economy). Where economic impacts occur between industries it should not be assumed that any impact is immediate as it will take some time for the impact to be integrated into the existing economy.

As this is only a model of the real world, it is likely that real-world results would differ from what is shown in this table. .id and NIEIR take no responsibility for the use of this information.

Definitions

Direct impacts: represent the initial change in the industry selected. This refers to expenditure associated with the industry (e.g. labour, material, supplies, capital).

Indirect impacts (Industrial): The direct impacts from the initial expenditure creates additional activity in the local economy (‘ripple effect’. Indirect effects are the results of business-to-business transactions indirectly caused by the direct impacts.

Induced impacts (Consumption): An increase in revenue (from direct and indirect impacts) means that businesses increase wages and salaries by hiring more employees, increasing hours worked and raising wages. Households will then increase spending at local businesses.

Value of Agricultural Commodities produced

The Agricultural Census is run by the Australian Bureau of Statistics every 5 years, with the collection period normally coinciding with the Census of Population and Housing, and the reference period being the previous financial year. The Agricultural Census covers all agricultural establishments with an Estimated Value of Agricultural Output (EVAO) of $50,000 or more in the reference year. Though it is a Census, there is usually less than 100% response, and some estimates are subject to sampling error.

Value of Agricultural Commodities data represent the gross value of production, derived from the Agricultural Census collection using estimates of production and average unit values of any given commodity in the marketplace over the reference year. Costs of production and marketing are not taken into account as these figures are gross values.

Due to changes in the agricultural classifications, time-series before 2015-16 are not currently shown on this site. For the 2015-16 estimates, users are advised to treat these as a guide to the main agricultural commodities only and should treat specific numbers with due caution.

Please note that in Western Australia some agricultural commodities are suppressed for confidentiality reasons. Totals have been derived but should be used carefully as they may not reflect the exact agricultural output in some areas.

Event impact model

The event calculator works by estimating the impact a user defined level of spend has across a range of event related industries. The industries included in the calculator are those that research shows have the highest level of direct economic impact that can be attributed to the running of an event in the RDA Darling Downs and South West Region. The estimated total spend of an event is broken down across the following industries based on the proportion of spend that can be attributed to each industry.

Industries included in the calculator:

Food Retailing

Other Retailing

Accommodation

Food and Beverage Services

Road Transport

Arts and Heritage

Sports and Recreation Activities

The proportion of spend allocated to each industry is dependent on the significance of the event, and they type of event which is determine by the user.

Event Significance

The significance of event is based on how far participants are prepared to travel to attend an event. An event can be classified into one of the following three significance levels.

Local - An event of local significance is assumed to attract attendance primarily from people who reside in the RDA Darling Downs and South West Region and the neighbouring local government areas.

Region - An event of regional significance is assumed to attract attendance primarily from people who reside within 300-500 km of the location of the event.

State - an event of state significance is assumed to draw attendance from people across the state and the rest of Australia.

Event type

The event type is determined by the primary focus of the event. An event can be classified as one of two types of events, Arts and Heritage events (.e.g. music concert, market) or Sports and Recreation events (e.g. cycle race, fun run)

The Event Calculator has been designed primarily to give an indication of the potential impact of a small to medium size event that generates a total of between $25,000 and $250,000 of spend by the participants. Significant related costs that can be assumed would not occur within the RDA Darling Downs and South West Region, such as domestic airfares, should not be included in the average daily spend figure. A small proportion of leakage of spend out of the local area is assumed in the calculation.

Location Quotient

The location quotient is a simple way of seeing which are the main industries in an area, relative to the wider region. LQ shows the percentage of the local economy characteristic (eg. employment, value add) in a particular industry divided by the percentage of the wider area (region, state, nation) that this industry makes up. It is derived as follows:

Local industry share = Industry value local area / Total Industries value local area

An LQ of exactly 1 means that industry is exactly as prevalent as in the benchmark region. An LQ above or below 1 highlights specialisations or lack thereof.

LQ < 0.8 - Indicates an industry which is more important in the benchmark region than the local area, and may represent an economic weakness or opportunity for growth.

0.8 < LQ < 1.2 - Indicates the industry is broadly similar in importance in the local area compared to the benchmark region.

LQ > 1.2 - Indicates the industry is a significant specialisation in the local area – possibly a key economic strength. Higher numbers mean greater specialisations. Anything over 2 is a major specialisation.

LQs should be analysed in combination with the proportional economic share that industry represents. For example, an industry with an LQ of 2 reveals a specialisation but if that industry only represents 3% of the local economy, it may not be significant.

Changes to historical data

This dataset is underpinned by the NIEIR-ID economic model which is updated each financial year. In the 2016-17 update you can expect to see differences in some of the numbers to previous updates. For more details, see Economic model updates.

Value of tourism

The tourism and hospitality industries are estimated from the NIEIR microsimulation model by looking at the level of exports from specific industries which have a significant direct tourism and hospitality component. By measuring the level of export activity (i.e. goods and services purchased by individuals or business from outside the local area) for those industries that form part of a Tourism and hospitality cluster, the value of the tourism and hospitality industry can be estimated.

Of the 86 industries at the 2 digit ANZSIC code, 11 industries have a signification direct tourism output. There are also many other industries that contribute to tourism indirectly such as transport and education. The value of these industries to the economy is taken into account through the calculation of their indirect impact tourism spend flows through the local economy.

Using this methodology the total sum of all regional Tourism output comes to within 5% of the ABS Tourism Satellite account. Using a regional methodology such as this applied nationally reduces the likelihood of over estimating the impact of tourisms to local areas, as the sum of all regional tourism output cannot exceed the established national benchmark.

Direct Tourism industries

1 Digit Industry

2 Digit Industry

Retail Trade

Motor Vehicle and Motor Vehicle Parts Retailing

Retail Trade

Fuel Retailing

Retail Trade

Food Retailing

Retail Trade

Other Store-Based Retailing

Retail Trade

Non-Store Retailing and Retail Commission Based Buying

Accommodation and Food Services

Accommodation

Accommodation and Food Services

Food and Beverage Services

Arts and Recreation Services

Heritage Activities

Arts and Recreation Services

Creative and Performing Arts Activities

Arts and Recreation Services

Sports and Recreation Activities

Arts and Recreation Services

Gambling Activities

Source: NIEIR microsimulation tourism model

Other industries that contribute to tourism

Industry

2 Digit Industry

Rental, Hiring and Real Estate Services

Residential property operators

Rail transport

Rail transport

Road transport

Taxi and other road transport

Road transport

Road freight transport

Road transport

Interurban and rural bus transport

Road transport

Urban bus transport (including tramway)

Water Transport

Water transport

Air and space transport

Air and space transport

Other transport

Scenic and Sightseeing Transport

Rental and Hiring Services (except Real Estate)

Passenger car rental and hiring

Administrative and Support Services services

Travel agency and tour arrangement

Education and training

Preschool and school education

Education and training

Tertiary education

Education and training

Adult, community and other education

Source: ABS Tourism Satellite account

Shift-share analysis

Shift Share Analysis provides a useful mechanism for better interpreting changes in economic variables between different time periods. It is a way of breaking the growth or decline in an industry into three components to help understand what is driving the change. These three change components are commonly known as:

National/State growth effect (NS) - the amount of growth or decline in an industry that could be attributed to the overall growth of a larger area that encompasses the region's economy, usually state or national.

Industry mix effect (IM) - the amount of growth or decline in an industry that could be attributed to the performance of the specific industry at the national/state level

Regional competitive effect (RS) - the amount of growth or decline in a specific industry that could be attributed to a local advantage or disadvantage. This is generally the most interesting component as it clearly quantifies the level of advantage or disadvantage an industry has in the local area.

The three components for a time period Year 1 to Year 2 for a given local area within a larger benchmark area are simply derived as follows:

A positive regional competitive effect for an industry generally indicates the local industry is outperforming benchmark state/national trends (both overall economic trends and trends in that specific industry). A negative effect means that the industry is under performing compared to benchmark trends.

The sign (+/-) of a regional competitive effect does not necessarily match the sign of the net change in the variable being measured over the given time period. For example, a local area may have a positive net change in an industry value (output, value add, jobs) but record a negative regional competitive effect. This means that the industry is growing strongly at the national/state level, stronger than in the given local area, and this is then the main cause of the local industry growth.

Shift-share analysis needs to be combined with other local area data (e.g. population growth, building approvals) and specific area knowledge to ascertain what may have caused the area to grow above or below trend.

Changes to historical data

This dataset is underpinned by the NIEIR-ID economic model which is updated each financial year. In the 2016-17 update you can expect to see differences in some of the numbers to previous updates. For more details, see Economic model updates.

Tourism visitor summary

Tourism Research Australia conduct two major annual surveys for the purpose of promoting and understanding the Australian Tourism Market.

The International Visitor Survey (IVS) samples 40,000 short-term international travellers aged over 15 when they leave Australia. It contains approximately 100 questions and is interviewer-based. The primary purpose of this survey is to derive reliable estimates of visitors by country of origin, reason for visit and expenditure at the Tourism Region (TR) level. Results for smaller areas are available and shown here, but they are subject to sampling error.

The National Visitor Survey (NVS) is conducted annually by telephone survey of approximately 120,000 Australian residents. It contains over 70 questions relating to travel within Australia by Australian residents, including destinations, purpose of trip, transportation, activities, expenditure and accommodation. This survey outputs data on overnight trips (including length of trip) and day trips by destination. Selected data from this survey are shown here. Details on the level of sampling error and further information on the methodology from the survey can be found the Tourism Research Australia National Visitor Survey Methodology(opens a new window) page.

The results from each of these surveys are weighted by population and demographics to produce estimates of total visitation shown in this topic.

Results are produced by Tourism Research Australia from the IVS and NVS at the Tourism Region level, and at the SA2 level. SA2s do not always align to LGA boundaries, and in those cases, to derive the LGA level estimates shown in economy.id, a concordance has been used which apportions the SA2 to the LGA of interest based on an estimate of the total number of businesses on either side of a boundary. This is necessarily an approximation, but as these results are surveys based on respondents recollections of travel rather than absolute boundaries, it is not expected to have a major impact on the results for most LGAs.

Confidentiality and data reliability

To protect the confidentiality of individual respondents, and due to concerns about unreliable data due to small sample sizes, Tourism Research Australia requires the suppression of all data items from the IVS and NVS based on a sample size of less than 40. Though actual sample sizes are not shown on this site, this data suppression has been actioned. Data marked with a "-" have been suppressed because they are based on small and unreliable samples. In some cases, individual financial years' data have been suppressed for this reason, but a 5 year average may still be able to be published. This means that time series cannot be shown, but it still allows the user to gain an understanding of broad tourism patterns in the area.

Additional datasets may be available using different combinations of geography or categories, even where the data shown here has been suppressed. .id has access to the TRA Online database and we are happy to help our clients with custom data requests where they will help inform the tourism picture of the area. Please contact .id for more information.

Legal Statement

In addition to the above information, Tourism Research Australia legally require the following statement to be published with this dataset:

This work is copyright. In addition to any use permitted under the Commonwealth Copyright Act 1968, the Commonwealth through Tourism Research Australia permits copies to be made in whole or in part for the purpose of promoting Australian tourism, provided that Tourism Research Australia (representing the Commonwealth) is identified on any copies as the author and the material is reproduced in its current form.

Copies may not be made for a commercial purpose, that is, for sale, without the permission of Tourism Research Australia (representing the Commonwealth). The information in this data is presented in good faith and on the basis that neither the Commonwealth, nor its agents or employees, are liable (whether by reason of error, omission, negligence, lack of care or otherwise) to any person for any damage or loss whatsoever which has occurred or may occur in relation to that person taking or not taking (as the case may be) action in respect of any statement, information or advice given in this publication.

Data derived from Tourism Research Australia surveys are subject to sample error. Users of the data are advised to consult the sample error tables contained in Tourism Research Australia publications or otherwise available from Tourism Research Australia before drawing any conclusions or inferences, or taking any action, based on the data.

SA2s used in this report to build the LGA boundary for the tourism visitor pages are:

Free demographic resources

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